{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## connectionist models are not reliable for certain tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.2311\n",
      "Epoch 2/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.2169\n",
      "Epoch 3/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.2082\n",
      "Epoch 4/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.2026\n",
      "Epoch 5/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1970\n",
      "Epoch 6/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1914\n",
      "Epoch 7/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1862\n",
      "Epoch 8/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1805\n",
      "Epoch 9/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1744\n",
      "Epoch 10/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.1690\n",
      "Epoch 11/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1632\n",
      "Epoch 12/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1581\n",
      "Epoch 13/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.1531\n",
      "Epoch 14/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1480\n",
      "Epoch 15/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1432\n",
      "Epoch 16/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1385\n",
      "Epoch 17/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.1339\n",
      "Epoch 18/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1287\n",
      "Epoch 19/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1240\n",
      "Epoch 20/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1195\n",
      "Epoch 21/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1150\n",
      "Epoch 22/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1121\n",
      "Epoch 23/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.1098\n",
      "Epoch 24/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1072\n",
      "Epoch 25/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.1042\n",
      "Epoch 26/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.1021\n",
      "Epoch 27/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.1001\n",
      "Epoch 28/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0986\n",
      "Epoch 29/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0974\n",
      "Epoch 30/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0963\n",
      "Epoch 31/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0950\n",
      "Epoch 32/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0937\n",
      "Epoch 33/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0925\n",
      "Epoch 34/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0911\n",
      "Epoch 35/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0899\n",
      "Epoch 36/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0892\n",
      "Epoch 37/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0880\n",
      "Epoch 38/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0867\n",
      "Epoch 39/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0856\n",
      "Epoch 40/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0852\n",
      "Epoch 41/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0846\n",
      "Epoch 42/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0836\n",
      "Epoch 43/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0823\n",
      "Epoch 44/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0813\n",
      "Epoch 45/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0802\n",
      "Epoch 46/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0797\n",
      "Epoch 47/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0792\n",
      "Epoch 48/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0791\n",
      "Epoch 49/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0784\n",
      "Epoch 50/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0772\n",
      "Epoch 51/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0759\n",
      "Epoch 52/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0749\n",
      "Epoch 53/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0744\n",
      "Epoch 54/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0738\n",
      "Epoch 55/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0735\n",
      "Epoch 56/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0723\n",
      "Epoch 57/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0708\n",
      "Epoch 58/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0708\n",
      "Epoch 59/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0702\n",
      "Epoch 60/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0693\n",
      "Epoch 61/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0682\n",
      "Epoch 62/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0675\n",
      "Epoch 63/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0668\n",
      "Epoch 64/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0661\n",
      "Epoch 65/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0651\n",
      "Epoch 66/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0646\n",
      "Epoch 67/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0640\n",
      "Epoch 68/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0632\n",
      "Epoch 69/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0628\n",
      "Epoch 70/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0624\n",
      "Epoch 71/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0618\n",
      "Epoch 72/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0614\n",
      "Epoch 73/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0610\n",
      "Epoch 74/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0605\n",
      "Epoch 75/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0601\n",
      "Epoch 76/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0597\n",
      "Epoch 77/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0591\n",
      "Epoch 78/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0592\n",
      "Epoch 79/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0591\n",
      "Epoch 80/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0584\n",
      "Epoch 81/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0581\n",
      "Epoch 82/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0576\n",
      "Epoch 83/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0571\n",
      "Epoch 84/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0569\n",
      "Epoch 85/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0565\n",
      "Epoch 86/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0561\n",
      "Epoch 87/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0557\n",
      "Epoch 88/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0558\n",
      "Epoch 89/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0556\n",
      "Epoch 90/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0550\n",
      "Epoch 91/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0549\n",
      "Epoch 92/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0546\n",
      "Epoch 93/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0540\n",
      "Epoch 94/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0539\n",
      "Epoch 95/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0534\n",
      "Epoch 96/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0533\n",
      "Epoch 97/1000\n",
      "4/4 [==============================] - 0s 3ms/step - loss: 0.0532\n",
      "Epoch 98/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0528\n",
      "Epoch 99/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0528\n",
      "Epoch 100/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0523\n",
      "Epoch 101/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0519\n",
      "Epoch 102/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0517\n",
      "Epoch 103/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0519\n",
      "Epoch 104/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0517\n",
      "Epoch 105/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0507\n",
      "Epoch 106/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0520\n",
      "Epoch 107/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0524\n",
      "Epoch 108/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0507\n",
      "Epoch 109/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0508\n",
      "Epoch 110/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0503\n",
      "Epoch 111/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0498\n",
      "Epoch 112/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0499\n",
      "Epoch 113/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0493\n",
      "Epoch 114/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0493\n",
      "Epoch 115/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0488\n",
      "Epoch 116/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0487\n",
      "Epoch 117/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0489\n",
      "Epoch 118/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0485\n",
      "Epoch 119/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0480\n",
      "Epoch 120/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0483\n",
      "Epoch 121/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0480\n",
      "Epoch 122/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0476\n",
      "Epoch 123/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0473\n",
      "Epoch 124/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0466\n",
      "Epoch 125/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0468\n",
      "Epoch 126/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0474\n",
      "Epoch 127/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0465\n",
      "Epoch 128/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0455\n",
      "Epoch 129/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0466\n",
      "Epoch 130/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0460\n",
      "Epoch 131/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0450\n",
      "Epoch 132/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0447\n",
      "Epoch 133/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0445\n",
      "Epoch 134/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0440\n",
      "Epoch 135/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0437\n",
      "Epoch 136/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0438\n",
      "Epoch 137/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0439\n",
      "Epoch 138/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0440\n",
      "Epoch 139/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0431\n",
      "Epoch 140/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0427\n",
      "Epoch 141/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0426\n",
      "Epoch 142/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0420\n",
      "Epoch 143/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0422\n",
      "Epoch 144/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0433\n",
      "Epoch 145/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0425\n",
      "Epoch 146/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0409\n",
      "Epoch 147/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0408\n",
      "Epoch 148/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0406\n",
      "Epoch 149/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0403\n",
      "Epoch 150/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0402\n",
      "Epoch 151/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0402\n",
      "Epoch 152/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0400\n",
      "Epoch 153/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0397\n",
      "Epoch 154/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0401\n",
      "Epoch 155/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0402\n",
      "Epoch 156/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0397\n",
      "Epoch 157/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0388\n",
      "Epoch 158/1000\n",
      "4/4 [==============================] - 0s 2ms/step - loss: 0.0400\n",
      "Epoch 159/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0400\n",
      "Epoch 160/1000\n",
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0382\n",
      "Epoch 161/1000\n",
      "1/4 [======>.......................] - ETA: 0s - loss: 0.0436"
     ]
    }
   ],
   "source": [
    "\"\"\"Neural net to take the product of three numbers \n",
    "After https://gist.github.com/qpwo/34983724ed9333225974191798bb672d\n",
    "\"\"\"\n",
    "from tensorflow.keras import models, layers\n",
    "import numpy as np\n",
    "\n",
    "np.set_printoptions(formatter={'float': lambda x: \"{0:0.3f}\".format(x)})\n",
    "\n",
    "x = np.random.random((100, 3))\n",
    "y = np.prod(x, axis=1).reshape(100, 1)\n",
    "\n",
    "model = models.Sequential([\n",
    "    layers.Dense(3, activation='relu', input_shape=(3,)),\n",
    "    layers.Dense(3, activation='relu'),\n",
    "    layers.Dense(3, activation='relu'),\n",
    "    layers.Dense(1, activation='relu')\n",
    "])\n",
    "model.compile(loss='mean_absolute_error', optimizer='adam')\n",
    "model.fit(x, y, epochs=1000)\n",
    "\n",
    "# small test, to see quality of results\n",
    "\n",
    "x2 = np.random.random((5, 3))\n",
    "y2 = np.prod(x2, axis=1).reshape(5,1)\n",
    "p2 = model.predict(x2)\n",
    "\n",
    "print(\"x:\\n\", x2)\n",
    "print(\"y:\\n\", y2)\n",
    "print(\"predicted:\\n\", p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import *\n",
    "from nltk.sem.drt import DrtParser\n",
    "from nltk.sem import logic\n",
    "logic._counter._value = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You need to install prover9 for the following code to work.\n",
    "\n",
    "On ubuntu/debian systems:\n",
    "```bash\n",
    "sudo apt install prover9\n",
    "```\n",
    "\n",
    "On macos:\n",
    "```bash\n",
    "brew install prover9\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from http://www.nltk.org/howto/inference.html\n",
    "from nltk.sem import Expression\n",
    "p1 = Expression.fromstring('man(socrates)')\n",
    "p2 = Expression.fromstring('all x.(man(x) -> mortal(x))')\n",
    "c  = Expression.fromstring('mortal(socrates)')\n",
    "Prover9().prove(c, [p1,p2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TableauProver().prove(c, [p1, p2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] {-mortal(socrates)}     A \n",
      "[2] {man(socrates)}         A \n",
      "[3] {-man(z2), mortal(z2)}  A \n",
      "[4] {-man(socrates)}        (1, 3) \n",
      "[5] {mortal(socrates)}      (2, 3) \n",
      "[6] {}                      (1, 5) \n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ResolutionProver().prove(c, [p1, p2], verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pyRDF2Vec\n",
      "  Downloading pyRDF2Vec-0.0.5.tar.gz (382 kB)\n",
      "\u001b[K     |████████████████████████████████| 382 kB 2.2 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: gensim in /Users/ben/anaconda3/lib/python3.6/site-packages (from pyRDF2Vec) (3.8.1)\n",
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      "Requirement already satisfied: pandas in /Users/ben/anaconda3/lib/python3.6/site-packages (from pyRDF2Vec) (1.1.0)\n",
      "Collecting rdflib\n",
      "  Downloading rdflib-5.0.0-py3-none-any.whl (231 kB)\n",
      "\u001b[K     |████████████████████████████████| 231 kB 12.9 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: scikit_learn in /Users/ben/anaconda3/lib/python3.6/site-packages (from pyRDF2Vec) (0.23.2)\n",
      "Requirement already satisfied: tqdm in /Users/ben/.local/lib/python3.6/site-packages (from pyRDF2Vec) (4.47.0)\n",
      "Requirement already satisfied: python-louvain in /Users/ben/anaconda3/lib/python3.6/site-packages (from pyRDF2Vec) (0.11)\n",
      "Requirement already satisfied: smart-open>=1.8.1 in /Users/ben/anaconda3/lib/python3.6/site-packages (from gensim->pyRDF2Vec) (1.9.0)\n",
      "Requirement already satisfied: six>=1.5.0 in /Users/ben/anaconda3/lib/python3.6/site-packages (from gensim->pyRDF2Vec) (1.15.0)\n",
      "Requirement already satisfied: scipy>=0.18.1 in /Users/ben/anaconda3/lib/python3.6/site-packages (from gensim->pyRDF2Vec) (1.5.2)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Users/ben/anaconda3/lib/python3.6/site-packages (from matplotlib->pyRDF2Vec) (2.4.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/ben/anaconda3/lib/python3.6/site-packages (from matplotlib->pyRDF2Vec) (0.10.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/ben/anaconda3/lib/python3.6/site-packages (from matplotlib->pyRDF2Vec) (1.0.1)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /Users/ben/anaconda3/lib/python3.6/site-packages (from matplotlib->pyRDF2Vec) (2.8.1)\n",
      "Requirement already satisfied: decorator>=4.3.0 in /Users/ben/anaconda3/lib/python3.6/site-packages (from networkx->pyRDF2Vec) (4.4.2)\n",
      "Requirement already satisfied: pytz>=2017.2 in /Users/ben/anaconda3/lib/python3.6/site-packages (from pandas->pyRDF2Vec) (2018.7)\n",
      "Requirement already satisfied: isodate in /Users/ben/anaconda3/lib/python3.6/site-packages (from rdflib->pyRDF2Vec) (0.6.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/ben/anaconda3/lib/python3.6/site-packages (from scikit_learn->pyRDF2Vec) (2.1.0)\n",
      "Requirement already satisfied: joblib>=0.11 in /Users/ben/anaconda3/lib/python3.6/site-packages (from scikit_learn->pyRDF2Vec) (0.16.0)\n",
      "Requirement already satisfied: boto>=2.32 in /Users/ben/anaconda3/lib/python3.6/site-packages (from smart-open>=1.8.1->gensim->pyRDF2Vec) (2.49.0)\n",
      "Requirement already satisfied: boto3 in /Users/ben/anaconda3/lib/python3.6/site-packages (from smart-open>=1.8.1->gensim->pyRDF2Vec) (1.9.70)\n",
      "Requirement already satisfied: requests in /Users/ben/anaconda3/lib/python3.6/site-packages (from smart-open>=1.8.1->gensim->pyRDF2Vec) (2.24.0)\n",
      "Requirement already satisfied: setuptools in /Users/ben/anaconda3/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib->pyRDF2Vec) (49.6.0.post20200814)\n",
      "Collecting botocore<1.13.0,>=1.12.70\n",
      "  Downloading botocore-1.12.253-py2.py3-none-any.whl (5.7 MB)\n",
      "\u001b[K     |████████████████████████████████| 5.7 MB 3.5 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: jmespath<1.0.0,>=0.7.1 in /Users/ben/anaconda3/lib/python3.6/site-packages (from boto3->smart-open>=1.8.1->gensim->pyRDF2Vec) (0.9.3)\n",
      "Requirement already satisfied: s3transfer<0.2.0,>=0.1.10 in /Users/ben/anaconda3/lib/python3.6/site-packages (from boto3->smart-open>=1.8.1->gensim->pyRDF2Vec) (0.1.13)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /Users/ben/anaconda3/lib/python3.6/site-packages (from requests->smart-open>=1.8.1->gensim->pyRDF2Vec) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /Users/ben/anaconda3/lib/python3.6/site-packages (from requests->smart-open>=1.8.1->gensim->pyRDF2Vec) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/ben/anaconda3/lib/python3.6/site-packages (from requests->smart-open>=1.8.1->gensim->pyRDF2Vec) (2020.6.20)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /Users/ben/anaconda3/lib/python3.6/site-packages (from requests->smart-open>=1.8.1->gensim->pyRDF2Vec) (1.25.10)\n",
      "Requirement already satisfied: docutils<0.16,>=0.10 in /Users/ben/anaconda3/lib/python3.6/site-packages (from botocore<1.13.0,>=1.12.70->boto3->smart-open>=1.8.1->gensim->pyRDF2Vec) (0.14)\n",
      "Building wheels for collected packages: pyRDF2Vec\n",
      "  Building wheel for pyRDF2Vec (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for pyRDF2Vec: filename=pyRDF2Vec-0.0.5-py3-none-any.whl size=13333 sha256=f65e3155f75c4f6bd1aabc1c61a47373d3bfe8eb8c6c6638c343daf1b6800bff\n",
      "  Stored in directory: /Users/ben/Library/Caches/pip/wheels/67/c9/df/e2cfc522ff6cc3f5e3978ad09e759698e9e16ff081d7e76db8\n",
      "Successfully built pyRDF2Vec\n",
      "Installing collected packages: rdflib, pyRDF2Vec, botocore\n",
      "  Attempting uninstall: botocore\n",
      "    Found existing installation: botocore 1.17.51\n",
      "    Uninstalling botocore-1.17.51:\n",
      "      Successfully uninstalled botocore-1.17.51\n",
      "\u001b[31mERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.\n",
      "\n",
      "We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.\n",
      "\n",
      "streamlit 0.65.2 requires botocore>=1.13.44, but you'll have botocore 1.12.253 which is incompatible.\u001b[0m\n",
      "Successfully installed botocore-1.12.253 pyRDF2Vec-0.0.5 rdflib-5.0.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pyRDF2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdf2vec.converters import create_kg\n",
    "import pandas as pd\n",
    "\n",
    "# get the zoo dataset from https://www.kaggle.com/uciml/zoo-animal-classification#\n",
    "zoo = pd.read_csv('zoo.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['animal_name',\n",
       " 'hair',\n",
       " 'feathers',\n",
       " 'eggs',\n",
       " 'milk',\n",
       " 'airborne',\n",
       " 'aquatic',\n",
       " 'predator',\n",
       " 'toothed',\n",
       " 'backbone',\n",
       " 'breathes',\n",
       " 'venomous',\n",
       " 'fins',\n",
       " 'legs',\n",
       " 'tail',\n",
       " 'domestic',\n",
       " 'catsize',\n",
       " 'class_type']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = list(zoo.columns)\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator',\n",
       "       'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'tail',\n",
       "       'domestic', 'catsize'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.columns[zoo.nunique() == 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['hair', 'feathers', 'eggs', 'milk', 'airborne', 'aquatic', 'predator',\n",
       "       'toothed', 'backbone', 'breathes', 'venomous', 'fins', 'tail',\n",
       "       'domestic', 'catsize'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary_cols = zoo.columns[zoo.nunique() == 2]\n",
    "binary_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in binary_cols:\n",
    "    zoo[col] = zoo[col].astype(bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "animal_name    object\n",
       "hair             bool\n",
       "feathers         bool\n",
       "eggs             bool\n",
       "milk             bool\n",
       "airborne         bool\n",
       "aquatic          bool\n",
       "predator         bool\n",
       "toothed          bool\n",
       "backbone         bool\n",
       "breathes         bool\n",
       "venomous         bool\n",
       "fins             bool\n",
       "legs            int64\n",
       "tail             bool\n",
       "domestic         bool\n",
       "catsize          bool\n",
       "class_type      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(zoo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>animal_name</th>\n",
       "      <th>hair</th>\n",
       "      <th>feathers</th>\n",
       "      <th>eggs</th>\n",
       "      <th>milk</th>\n",
       "      <th>airborne</th>\n",
       "      <th>aquatic</th>\n",
       "      <th>predator</th>\n",
       "      <th>toothed</th>\n",
       "      <th>backbone</th>\n",
       "      <th>breathes</th>\n",
       "      <th>venomous</th>\n",
       "      <th>fins</th>\n",
       "      <th>legs</th>\n",
       "      <th>tail</th>\n",
       "      <th>domestic</th>\n",
       "      <th>catsize</th>\n",
       "      <th>class_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aardvark</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>antelope</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bass</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bear</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>boar</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  animal_name   hair  feathers   eggs   milk  airborne  aquatic  predator  \\\n",
       "0    aardvark   True     False  False   True     False    False      True   \n",
       "1    antelope   True     False  False   True     False    False     False   \n",
       "2        bass  False     False   True  False     False     True      True   \n",
       "3        bear   True     False  False   True     False    False      True   \n",
       "4        boar   True     False  False   True     False    False      True   \n",
       "\n",
       "   toothed  backbone  breathes  venomous   fins  legs   tail  domestic  \\\n",
       "0     True      True      True     False  False     4  False     False   \n",
       "1     True      True      True     False  False     4   True     False   \n",
       "2     True      True     False     False   True     0   True     False   \n",
       "3     True      True      True     False  False     4  False     False   \n",
       "4     True      True      True     False  False     4   True     False   \n",
       "\n",
       "   catsize  class_type  \n",
       "0     True           1  \n",
       "1     True           1  \n",
       "2    False           4  \n",
       "3     True           1  \n",
       "4     True           1  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_size = int(len(zoo) * 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_labels = {\n",
    "    i+1: c for i, c in enumerate(\n",
    "        [\n",
    "        'Mammal',\n",
    "        'Bird',\n",
    "        'Reptile',\n",
    "        'Fish',\n",
    "        'Amphibian',\n",
    "        'Bug',\n",
    "        'Invertebrate'\n",
    "        ])\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdf2vec.graph import KnowledgeGraph, Vertex\n",
    "\n",
    "\n",
    "def get_triplets(row, col):\n",
    "    \"\"\"return triplets for binary and integer elements\n",
    "    \n",
    "    Format: (s, p, o)\n",
    "    \"\"\"\n",
    "    if col == 'class_type':\n",
    "        return  (\n",
    "            all_labels[row[col]],\n",
    "            'is_a',  # 'has' + col\n",
    "            row['animal_name'],\n",
    "        )\n",
    "    # integer properties:\n",
    "    if col in ['legs']:\n",
    "        #if row[col] > 0:\n",
    "        return (\n",
    "            row['animal_name'],\n",
    "            'has' + col,\n",
    "            str(row[col]) + '_legs'\n",
    "        )\n",
    "        #else:\n",
    "        #    return ()\n",
    "    # binary properties:\n",
    "    if row[col]:\n",
    "        return (\n",
    "            row['animal_name'],\n",
    "            'has',  # if row[col] else 'hasno' + col,\n",
    "            str(col)\n",
    "        )\n",
    "    else:\n",
    "        return ()\n",
    "\n",
    "triplets = []\n",
    "for i, row in zoo.iterrows():\n",
    "    for col in cols:\n",
    "        if col == 'animal_name':\n",
    "            continue\n",
    "        if col == 'class_type' and i > training_size:\n",
    "                continue\n",
    "        triplet = get_triplets(row, col)\n",
    "        if triplet:\n",
    "            triplets.append(triplet)\n",
    "    #if i > 5:\n",
    "    #    break\n",
    "\n",
    "\n",
    "label_predicates = [] #[ 'is_a' ]\n",
    "kg = KnowledgeGraph()\n",
    "for (s, p, o) in triplets:\n",
    "    if p not in label_predicates:\n",
    "        s_v = Vertex(str(s))\n",
    "        o_v = Vertex(str(o))\n",
    "        p_v = Vertex(str(p), predicate=True, _from=s_v, _to=o_v)\n",
    "        kg.add_vertex(s_v)\n",
    "        kg.add_vertex(p_v)\n",
    "        kg.add_vertex(o_v)\n",
    "        kg.add_edge(s_v, p_v)\n",
    "        kg.add_edge(p_v, o_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "842"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(triplets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('aardvark', 'has', 'hair'),\n",
       " ('aardvark', 'has', 'milk'),\n",
       " ('aardvark', 'has', 'predator'),\n",
       " ('aardvark', 'has', 'toothed'),\n",
       " ('aardvark', 'has', 'backbone'),\n",
       " ('aardvark', 'has', 'breathes'),\n",
       " ('aardvark', 'haslegs', '4_legs'),\n",
       " ('aardvark', 'has', 'catsize'),\n",
       " ('Mammal', 'is_a', 'aardvark'),\n",
       " ('antelope', 'has', 'hair'),\n",
       " ('antelope', 'has', 'milk'),\n",
       " ('antelope', 'has', 'toothed'),\n",
       " ('antelope', 'has', 'backbone'),\n",
       " ('antelope', 'has', 'breathes'),\n",
       " ('antelope', 'haslegs', '4_legs'),\n",
       " ('antelope', 'has', 'tail'),\n",
       " ('antelope', 'has', 'catsize'),\n",
       " ('Mammal', 'is_a', 'antelope'),\n",
       " ('bass', 'has', 'eggs'),\n",
       " ('bass', 'has', 'aquatic')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triplets[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('wasp', 'haslegs', '6_legs'),\n",
       " ('wolf', 'has', 'hair'),\n",
       " ('wolf', 'has', 'milk'),\n",
       " ('wolf', 'has', 'predator'),\n",
       " ('wolf', 'has', 'toothed'),\n",
       " ('wolf', 'has', 'backbone'),\n",
       " ('wolf', 'has', 'breathes'),\n",
       " ('wolf', 'haslegs', '4_legs'),\n",
       " ('wolf', 'has', 'tail'),\n",
       " ('wolf', 'has', 'catsize'),\n",
       " ('worm', 'has', 'eggs'),\n",
       " ('worm', 'has', 'breathes'),\n",
       " ('worm', 'haslegs', '0_legs'),\n",
       " ('wren', 'has', 'feathers'),\n",
       " ('wren', 'has', 'eggs'),\n",
       " ('wren', 'has', 'airborne'),\n",
       " ('wren', 'has', 'backbone'),\n",
       " ('wren', 'has', 'breathes'),\n",
       " ('wren', 'haslegs', '2_legs'),\n",
       " ('wren', 'has', 'tail')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "triplets[-20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 842/842 [00:00<00:00, 38857.09it/s]\n"
     ]
    }
   ],
   "source": [
    "kg = create_kg(triplets, label_predicates=label_predicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<rdf2vec.graph.KnowledgeGraph at 0x7ff49ce51128>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ben/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:563: MatplotlibDeprecationWarning: \n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "  if not cb.iterable(width):\n",
      "/Users/ben/anaconda3/lib/python3.6/site-packages/networkx/drawing/nx_pylab.py:660: MatplotlibDeprecationWarning: \n",
      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
      "  if cb.iterable(node_size):  # many node sizes\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kg.visualise()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "entities = list(zoo.animal_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracted 754 walks for 101 instances!\n"
     ]
    }
   ],
   "source": [
    "from rdf2vec import RDF2VecTransformer\n",
    "from rdf2vec.walkers import RandomWalker, CommunityWalker\n",
    "import numpy as np\n",
    "\n",
    "# We specify the depth and maximum number of walks per entity\n",
    "random_walker = RandomWalker(5, float('inf'))\n",
    "\n",
    "transformer = RDF2VecTransformer(vector_size=50, walkers=[random_walker], sg=1)\n",
    "# Entities should be a list of URIs that can be found in the KG\n",
    "embeddings = np.array(transformer.fit_transform(kg, entities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101, 50)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['slowworm', False, False, True, False, False, False, True, True,\n",
       "        True, True, False, False, 0, True, False, False],\n",
       "       ['slug', False, False, True, False, False, False, False, False,\n",
       "        False, True, False, False, 0, False, False, False],\n",
       "       ['sole', False, False, True, False, False, True, False, True,\n",
       "        True, False, False, True, 0, True, False, False],\n",
       "       ['sparrow', False, True, True, False, True, False, False, False,\n",
       "        True, True, False, False, 2, True, False, False],\n",
       "       ['squirrel', True, False, False, True, False, False, False, True,\n",
       "        True, True, False, False, 2, True, False, False],\n",
       "       ['starfish', False, False, True, False, False, True, True, False,\n",
       "        False, False, False, False, 5, False, False, False],\n",
       "       ['stingray', False, False, True, False, False, True, True, True,\n",
       "        True, False, True, True, 0, True, False, True],\n",
       "       ['swan', False, True, True, False, True, True, False, False, True,\n",
       "        True, False, False, 2, True, False, True],\n",
       "       ['termite', False, False, True, False, False, False, False, False,\n",
       "        False, True, False, False, 6, False, False, False],\n",
       "       ['toad', False, False, True, False, False, True, False, True,\n",
       "        True, True, False, False, 4, False, False, False],\n",
       "       ['tortoise', False, False, True, False, False, False, False,\n",
       "        False, True, True, False, False, 4, True, False, True],\n",
       "       ['tuatara', False, False, True, False, False, False, True, True,\n",
       "        True, True, False, False, 4, True, False, False],\n",
       "       ['tuna', False, False, True, False, False, True, True, True, True,\n",
       "        False, False, True, 0, True, False, True],\n",
       "       ['vampire', True, False, False, True, True, False, False, True,\n",
       "        True, True, False, False, 2, True, False, False],\n",
       "       ['vole', True, False, False, True, False, False, False, True,\n",
       "        True, True, False, False, 4, True, False, False],\n",
       "       ['vulture', False, True, True, False, True, False, True, False,\n",
       "        True, True, False, False, 2, True, False, True],\n",
       "       ['wallaby', True, False, False, True, False, False, False, True,\n",
       "        True, True, False, False, 2, True, False, True],\n",
       "       ['wasp', True, False, True, False, True, False, False, False,\n",
       "        False, True, True, False, 6, False, False, False],\n",
       "       ['wolf', True, False, False, True, False, False, True, True, True,\n",
       "        True, False, False, 4, True, False, True],\n",
       "       ['worm', False, False, True, False, False, False, False, False,\n",
       "        False, True, False, False, 0, False, False, False],\n",
       "       ['wren', False, True, True, False, True, False, False, False,\n",
       "        True, True, False, False, 2, True, False, False]], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.values[training_size:, :-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[True, False, False, ..., False, False, True],\n",
       "       [True, False, False, ..., True, False, True],\n",
       "       [False, False, True, ..., True, False, False],\n",
       "       ...,\n",
       "       [False, False, True, ..., False, False, False],\n",
       "       [False, True, True, ..., True, False, False],\n",
       "       [False, True, True, ..., True, False, False]], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoo.values[:training_size, 1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine: Accuracy = 0.23809523809523808\n",
      "[[5 0 0 0 0 0 0]\n",
      " [4 0 0 0 0 0 0]\n",
      " [3 0 0 0 0 0 0]\n",
      " [3 0 0 0 0 0 0]\n",
      " [1 0 0 0 0 0 0]\n",
      " [2 0 0 0 0 0 0]\n",
      " [3 0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score, confusion_matrix\n",
    ")\n",
    "\n",
    "# Fit a support vector machine on train embeddings and evaluate on test\n",
    "clf = SVC(random_state=42)\n",
    "clf.fit(embeddings[:training_size, :], zoo.class_type[:training_size])\n",
    "\n",
    "test_labels = zoo.class_type[training_size:]\n",
    "test_embeddings = embeddings[training_size:, :]\n",
    "print(end='Support Vector Machine: Accuracy = ')\n",
    "print(accuracy_score(test_labels, clf.predict(test_embeddings)))\n",
    "print(confusion_matrix(test_labels, clf.predict(test_embeddings)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "[color_map[i] for i in all_labels[:training_size]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine: Accuracy = 0.8571428571428571\n",
      "[[5 0 0 0 0 0 0]\n",
      " [0 4 0 0 0 0 0]\n",
      " [2 0 0 1 0 0 0]\n",
      " [0 0 0 3 0 0 0]\n",
      " [0 0 0 0 1 0 0]\n",
      " [0 0 0 0 0 2 0]\n",
      " [0 0 0 0 0 0 3]]\n"
     ]
    }
   ],
   "source": [
    "# baseline\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "\n",
    "\n",
    "clf = SVC(random_state=42)\n",
    "clf.fit(zoo.values[:training_size, 1:-1], zoo.class_type[:training_size])\n",
    "\n",
    "test_labels = zoo.class_type[training_size:]\n",
    "test_embeddings = zoo.values[training_size:, 1:-1]\n",
    "print(end='Support Vector Machine: Accuracy = ')\n",
    "print(accuracy_score(test_labels, clf.predict(test_embeddings)))\n",
    "print(confusion_matrix(test_labels, clf.predict(test_embeddings)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.01001132, -0.02525392,  0.06960275, ...,  0.15502866,\n",
       "         0.16011204,  0.0738394 ],\n",
       "       [-0.02124083, -0.03143897,  0.06947882, ...,  0.14999168,\n",
       "         0.17350586,  0.06461088],\n",
       "       [-0.01351023, -0.02223515,  0.0777923 , ...,  0.1656029 ,\n",
       "         0.2014776 ,  0.0701108 ],\n",
       "       ...,\n",
       "       [-0.01447095, -0.0330925 ,  0.07540778, ...,  0.15747508,\n",
       "         0.18476303,  0.07273882],\n",
       "       [-0.01615521, -0.03254588,  0.08525735, ...,  0.17520814,\n",
       "         0.19943546,  0.07954543],\n",
       "       [-0.00706413, -0.02800808,  0.08506058, ...,  0.16167592,\n",
       "         0.18534   ,  0.07644872]], dtype=float32)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings[:training_size, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting karateclub\n",
      "  Downloading karateclub-1.0.15.tar.gz (54 kB)\n",
      "\u001b[K     |████████████████████████████████| 54 kB 1.7 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting networkx\n",
      "  Downloading networkx-2.5-py3-none-any.whl (1.6 MB)\n",
      "\u001b[K     |████████████████████████████████| 1.6 MB 3.0 MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting node2vec\n",
      "  Downloading node2vec-0.3.3.tar.gz (4.3 kB)\n",
      "Collecting pygraphviz\n",
      "  Downloading pygraphviz-1.6.zip (117 kB)\n",
      "\u001b[K     |████████████████████████████████| 117 kB 3.9 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: numpy in /Users/ben/anaconda3/lib/python3.6/site-packages (from karateclub) (1.18.5)\n",
      "Requirement already satisfied, skipping upgrade: tqdm in /Users/ben/.local/lib/python3.6/site-packages (from karateclub) (4.47.0)\n",
      "Requirement already satisfied, skipping upgrade: python-louvain in /Users/ben/anaconda3/lib/python3.6/site-packages (from karateclub) (0.11)\n",
      "Requirement already satisfied, skipping upgrade: scikit-learn in /Users/ben/anaconda3/lib/python3.6/site-packages (from karateclub) (0.23.2)\n",
      "Requirement already satisfied, skipping upgrade: scipy in /Users/ben/anaconda3/lib/python3.6/site-packages (from karateclub) (1.5.2)\n",
      "Collecting pygsp\n",
      "  Downloading PyGSP-0.5.1-py2.py3-none-any.whl (1.8 MB)\n",
      "\u001b[K     |████████████████████████████████| 1.8 MB 2.6 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: gensim in /Users/ben/anaconda3/lib/python3.6/site-packages (from karateclub) (3.8.1)\n",
      "Requirement already satisfied, skipping upgrade: pandas in /Users/ben/anaconda3/lib/python3.6/site-packages (from karateclub) (1.1.0)\n",
      "Requirement already satisfied, skipping upgrade: six in /Users/ben/anaconda3/lib/python3.6/site-packages (from karateclub) (1.15.0)\n",
      "Requirement already satisfied, skipping upgrade: decorator>=4.3.0 in /Users/ben/anaconda3/lib/python3.6/site-packages (from networkx) (4.4.2)\n",
      "Requirement already satisfied, skipping upgrade: joblib>=0.13.2 in /Users/ben/anaconda3/lib/python3.6/site-packages (from node2vec) (0.16.0)\n",
      "Requirement already satisfied, skipping upgrade: threadpoolctl>=2.0.0 in /Users/ben/anaconda3/lib/python3.6/site-packages (from scikit-learn->karateclub) (2.1.0)\n",
      "Requirement already satisfied, skipping upgrade: smart-open>=1.8.1 in /Users/ben/anaconda3/lib/python3.6/site-packages (from gensim->karateclub) (1.9.0)\n",
      "Requirement already satisfied, skipping upgrade: python-dateutil>=2.7.3 in /Users/ben/anaconda3/lib/python3.6/site-packages (from pandas->karateclub) (2.8.1)\n",
      "Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /Users/ben/anaconda3/lib/python3.6/site-packages (from pandas->karateclub) (2018.7)\n",
      "Requirement already satisfied, skipping upgrade: boto3 in /Users/ben/anaconda3/lib/python3.6/site-packages (from smart-open>=1.8.1->gensim->karateclub) (1.9.70)\n",
      "Requirement already satisfied, skipping upgrade: boto>=2.32 in /Users/ben/anaconda3/lib/python3.6/site-packages (from smart-open>=1.8.1->gensim->karateclub) (2.49.0)\n",
      "Requirement already satisfied, skipping upgrade: requests in /Users/ben/anaconda3/lib/python3.6/site-packages (from smart-open>=1.8.1->gensim->karateclub) (2.24.0)\n",
      "Requirement already satisfied, skipping upgrade: botocore<1.13.0,>=1.12.70 in /Users/ben/anaconda3/lib/python3.6/site-packages (from boto3->smart-open>=1.8.1->gensim->karateclub) (1.12.253)\n",
      "Requirement already satisfied, skipping upgrade: jmespath<1.0.0,>=0.7.1 in /Users/ben/anaconda3/lib/python3.6/site-packages (from boto3->smart-open>=1.8.1->gensim->karateclub) (0.9.3)\n",
      "Requirement already satisfied, skipping upgrade: s3transfer<0.2.0,>=0.1.10 in /Users/ben/anaconda3/lib/python3.6/site-packages (from boto3->smart-open>=1.8.1->gensim->karateclub) (0.1.13)\n",
      "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /Users/ben/anaconda3/lib/python3.6/site-packages (from requests->smart-open>=1.8.1->gensim->karateclub) (3.0.4)\n",
      "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /Users/ben/anaconda3/lib/python3.6/site-packages (from requests->smart-open>=1.8.1->gensim->karateclub) (2020.6.20)\n",
      "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /Users/ben/anaconda3/lib/python3.6/site-packages (from requests->smart-open>=1.8.1->gensim->karateclub) (1.25.10)\n",
      "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /Users/ben/anaconda3/lib/python3.6/site-packages (from requests->smart-open>=1.8.1->gensim->karateclub) (2.10)\n",
      "Requirement already satisfied, skipping upgrade: docutils<0.16,>=0.10 in /Users/ben/anaconda3/lib/python3.6/site-packages (from botocore<1.13.0,>=1.12.70->boto3->smart-open>=1.8.1->gensim->karateclub) (0.14)\n",
      "Building wheels for collected packages: karateclub, node2vec, pygraphviz\n",
      "  Building wheel for karateclub (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for karateclub: filename=karateclub-1.0.15-py3-none-any.whl size=87934 sha256=edce8a4ad21ab76f9a24727203e9339bf88096ea09183104255bbd717e965cba\n",
      "  Stored in directory: /Users/ben/Library/Caches/pip/wheels/3a/8d/60/e48c4ad985f0e3aa94d7ef012800dc7f0f521be04fc9daa013\n",
      "  Building wheel for node2vec (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for node2vec: filename=node2vec-0.3.3-py3-none-any.whl size=5674 sha256=ce629c613a6d2427e59027ea4f34b5d645149529b96968a2463cccf312a5bdb1\n",
      "  Stored in directory: /Users/ben/Library/Caches/pip/wheels/41/60/64/18cdc6dcdaec46c0e9658d613cc915a934b79a6fbc286795f6\n",
      "  Building wheel for pygraphviz (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for pygraphviz: filename=pygraphviz-1.6-cp36-cp36m-macosx_10_7_x86_64.whl size=96965 sha256=2ade5bdaf3070fabc120ca6f79ccf887dbb2f1589acbd5ef1fbcb696e27c905f\n",
      "  Stored in directory: /Users/ben/Library/Caches/pip/wheels/ae/dd/00/72ba3f6cec9d5c207e8dde77052acf6c27a58afefa0775ce04\n",
      "Successfully built karateclub node2vec pygraphviz\n",
      "Installing collected packages: networkx, pygsp, karateclub, node2vec, pygraphviz\n",
      "  Attempting uninstall: networkx\n",
      "    Found existing installation: networkx 2.2\n",
      "    Uninstalling networkx-2.2:\n",
      "      Successfully uninstalled networkx-2.2\n",
      "  Attempting uninstall: pygraphviz\n",
      "    Found existing installation: pygraphviz 1.5\n",
      "    Uninstalling pygraphviz-1.5:\n",
      "      Successfully uninstalled pygraphviz-1.5\n",
      "Successfully installed karateclub-1.0.15 networkx-2.5 node2vec-0.3.3 pygraphviz-1.6 pygsp-0.5.1\n"
     ]
    }
   ],
   "source": [
    "!pip install -U karateclub networkx node2vec pygraphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx_graph = nx.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "nx_graph = nx.Graph()\n",
    "for (a, p, b) in triplets:\n",
    "    nx_graph.add_edge(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = nx.nx_agraph.to_agraph(nx_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "strict graph \"\" {\n",
      "\taardvark -- hair;\n",
      "\taardvark -- milk;\n",
      "\taardvark -- predator;\n",
      "\taardvark -- toothed;\n",
      "\taardvark -- backbone;\n",
      "\taardvark -- breathes;\n",
      "\taardvark -- \"4_legs\";\n",
      "\taardvark -- catsize;\n",
      "\taardvark -- Mammal;\n",
      "\thair -- antelope;\n",
      "\thair -- bear;\n",
      "\thair -- boar;\n",
      "\thair -- buffalo;\n",
      "\thair -- calf;\n",
      "\thair -- cavy;\n",
      "\thair -- cheetah;\n",
      "\thair -- deer;\n",
      "\thair -- elephant;\n",
      "\thair -- fruitbat;\n",
      "\thair -- giraffe;\n",
      "\thair -- girl;\n",
      "\thair -- goat;\n",
      "\thair -- gorilla;\n",
      "\thair -- hamster;\n",
      "\thair -- hare;\n",
      "\thair -- honeybee;\n",
      "\thair -- housefly;\n",
      "\thair -- leopard;\n",
      "\thair -- lion;\n",
      "\thair -- lynx;\n",
      "\thair -- mink;\n",
      "\thair -- mole;\n",
      "\thair -- mongoose;\n",
      "\thair -- moth;\n",
      "\thair -- opossum;\n",
      "\thair -- oryx;\n",
      "\thair -- platypus;\n",
      "\thair -- polecat;\n",
      "\thair -- pony;\n",
      "\thair -- puma;\n",
      "\thair -- pussycat;\n",
      "\thair -- raccoon;\n",
      "\thair -- reindeer;\n",
      "\thair -- seal;\n",
      "\thair -- sealion;\n",
      "\thair -- squirrel;\n",
      "\thair -- vampire;\n",
      "\thair -- vole;\n",
      "\thair -- wallaby;\n",
      "\thair -- wasp;\n",
      "\thair -- wolf;\n",
      "\tmilk -- antelope;\n",
      "\tmilk -- bear;\n",
      "\tmilk -- boar;\n",
      "\tmilk -- buffalo;\n",
      "\tmilk -- calf;\n",
      "\tmilk -- cavy;\n",
      "\tmilk -- cheetah;\n",
      "\tmilk -- deer;\n",
      "\tmilk -- elephant;\n",
      "\tmilk -- fruitbat;\n",
      "\tmilk -- giraffe;\n",
      "\tmilk -- girl;\n",
      "\tmilk -- goat;\n",
      "\tmilk -- gorilla;\n",
      "\tmilk -- hamster;\n",
      "\tmilk -- hare;\n",
      "\tmilk -- leopard;\n",
      "\tmilk -- lion;\n",
      "\tmilk -- lynx;\n",
      "\tmilk -- mink;\n",
      "\tmilk -- mole;\n",
      "\tmilk -- mongoose;\n",
      "\tmilk -- opossum;\n",
      "\tmilk -- oryx;\n",
      "\tmilk -- platypus;\n",
      "\tmilk -- polecat;\n",
      "\tmilk -- pony;\n",
      "\tmilk -- puma;\n",
      "\tmilk -- pussycat;\n",
      "\tmilk -- raccoon;\n",
      "\tmilk -- reindeer;\n",
      "\tmilk -- seal;\n",
      "\tmilk -- sealion;\n",
      "\tmilk -- squirrel;\n",
      "\tmilk -- vampire;\n",
      "\tmilk -- vole;\n",
      "\tmilk -- wallaby;\n",
      "\tmilk -- wolf;\n",
      "\tmilk -- dolphin;\n",
      "\tmilk -- porpoise;\n",
      "\tpredator -- bear;\n",
      "\tpredator -- boar;\n",
      "\tpredator -- cheetah;\n",
      "\tpredator -- girl;\n",
      "\tpredator -- leopard;\n",
      "\tpredator -- lion;\n",
      "\tpredator -- lynx;\n",
      "\tpredator -- mink;\n",
      "\tpredator -- mole;\n",
      "\tpredator -- mongoose;\n",
      "\tpredator -- opossum;\n",
      "\tpredator -- platypus;\n",
      "\tpredator -- polecat;\n",
      "\tpredator -- puma;\n",
      "\tpredator -- pussycat;\n",
      "\tpredator -- raccoon;\n",
      "\tpredator -- seal;\n",
      "\tpredator -- sealion;\n",
      "\tpredator -- wolf;\n",
      "\tpredator -- dolphin;\n",
      "\tpredator -- porpoise;\n",
      "\tpredator -- bass;\n",
      "\tpredator -- catfish;\n",
      "\tpredator -- chub;\n",
      "\tpredator -- clam;\n",
      "\tpredator -- crab;\n",
      "\tpredator -- crayfish;\n",
      "\tpredator -- crow;\n",
      "\tpredator -- dogfish;\n",
      "\tpredator -- frog;\n",
      "\tpredator -- gull;\n",
      "\tpredator -- hawk;\n",
      "\tpredator -- herring;\n",
      "\tpredator -- kiwi;\n",
      "\tpredator -- ladybird;\n",
      "\tpredator -- lobster;\n",
      "\tpredator -- newt;\n",
      "\tpredator -- octopus;\n",
      "\tpredator -- penguin;\n",
      "\tpredator -- pike;\n",
      "\tpredator -- piranha;\n",
      "\tpredator -- pitviper;\n",
      "\tpredator -- rhea;\n",
      "\tpredator -- scorpion;\n",
      "\tpredator -- seasnake;\n",
      "\tpredator -- seawasp;\n",
      "\tpredator -- skimmer;\n",
      "\tpredator -- skua;\n",
      "\tpredator -- slowworm;\n",
      "\tpredator -- starfish;\n",
      "\tpredator -- stingray;\n",
      "\tpredator -- tuatara;\n",
      "\tpredator -- tuna;\n",
      "\tpredator -- vulture;\n",
      "\ttoothed -- antelope;\n",
      "\ttoothed -- bear;\n",
      "\ttoothed -- boar;\n",
      "\ttoothed -- buffalo;\n",
      "\ttoothed -- calf;\n",
      "\ttoothed -- cavy;\n",
      "\ttoothed -- cheetah;\n",
      "\ttoothed -- deer;\n",
      "\ttoothed -- elephant;\n",
      "\ttoothed -- fruitbat;\n",
      "\ttoothed -- giraffe;\n",
      "\ttoothed -- girl;\n",
      "\ttoothed -- goat;\n",
      "\ttoothed -- gorilla;\n",
      "\ttoothed -- hamster;\n",
      "\ttoothed -- hare;\n",
      "\ttoothed -- leopard;\n",
      "\ttoothed -- lion;\n",
      "\ttoothed -- lynx;\n",
      "\ttoothed -- mink;\n",
      "\ttoothed -- mole;\n",
      "\ttoothed -- mongoose;\n",
      "\ttoothed -- opossum;\n",
      "\ttoothed -- oryx;\n",
      "\ttoothed -- polecat;\n",
      "\ttoothed -- pony;\n",
      "\ttoothed -- puma;\n",
      "\ttoothed -- pussycat;\n",
      "\ttoothed -- raccoon;\n",
      "\ttoothed -- reindeer;\n",
      "\ttoothed -- seal;\n",
      "\ttoothed -- sealion;\n",
      "\ttoothed -- squirrel;\n",
      "\ttoothed -- vampire;\n",
      "\ttoothed -- vole;\n",
      "\ttoothed -- wallaby;\n",
      "\ttoothed -- wolf;\n",
      "\ttoothed -- dolphin;\n",
      "\ttoothed -- porpoise;\n",
      "\ttoothed -- bass;\n",
      "\ttoothed -- catfish;\n",
      "\ttoothed -- chub;\n",
      "\ttoothed -- dogfish;\n",
      "\ttoothed -- frog;\n",
      "\ttoothed -- herring;\n",
      "\ttoothed -- newt;\n",
      "\ttoothed -- pike;\n",
      "\ttoothed -- piranha;\n",
      "\ttoothed -- pitviper;\n",
      "\ttoothed -- seasnake;\n",
      "\ttoothed -- slowworm;\n",
      "\ttoothed -- stingray;\n",
      "\ttoothed -- tuatara;\n",
      "\ttoothed -- tuna;\n",
      "\ttoothed -- carp;\n",
      "\ttoothed -- haddock;\n",
      "\ttoothed -- seahorse;\n",
      "\ttoothed -- sole;\n",
      "\ttoothed -- toad;\n",
      "\tbackbone -- antelope;\n",
      "\tbackbone -- bear;\n",
      "\tbackbone -- boar;\n",
      "\tbackbone -- buffalo;\n",
      "\tbackbone -- calf;\n",
      "\tbackbone -- cavy;\n",
      "\tbackbone -- cheetah;\n",
      "\tbackbone -- deer;\n",
      "\tbackbone -- elephant;\n",
      "\tbackbone -- fruitbat;\n",
      "\tbackbone -- giraffe;\n",
      "\tbackbone -- girl;\n",
      "\tbackbone -- goat;\n",
      "\tbackbone -- gorilla;\n",
      "\tbackbone -- hamster;\n",
      "\tbackbone -- hare;\n",
      "\tbackbone -- leopard;\n",
      "\tbackbone -- lion;\n",
      "\tbackbone -- lynx;\n",
      "\tbackbone -- mink;\n",
      "\tbackbone -- mole;\n",
      "\tbackbone -- mongoose;\n",
      "\tbackbone -- opossum;\n",
      "\tbackbone -- oryx;\n",
      "\tbackbone -- platypus;\n",
      "\tbackbone -- polecat;\n",
      "\tbackbone -- pony;\n",
      "\tbackbone -- puma;\n",
      "\tbackbone -- pussycat;\n",
      "\tbackbone -- raccoon;\n",
      "\tbackbone -- reindeer;\n",
      "\tbackbone -- seal;\n",
      "\tbackbone -- sealion;\n",
      "\tbackbone -- squirrel;\n",
      "\tbackbone -- vampire;\n",
      "\tbackbone -- vole;\n",
      "\tbackbone -- wallaby;\n",
      "\tbackbone -- wolf;\n",
      "\tbackbone -- dolphin;\n",
      "\tbackbone -- porpoise;\n",
      "\tbackbone -- bass;\n",
      "\tbackbone -- catfish;\n",
      "\tbackbone -- chub;\n",
      "\tbackbone -- crow;\n",
      "\tbackbone -- dogfish;\n",
      "\tbackbone -- frog;\n",
      "\tbackbone -- gull;\n",
      "\tbackbone -- hawk;\n",
      "\tbackbone -- herring;\n",
      "\tbackbone -- kiwi;\n",
      "\tbackbone -- newt;\n",
      "\tbackbone -- penguin;\n",
      "\tbackbone -- pike;\n",
      "\tbackbone -- piranha;\n",
      "\tbackbone -- pitviper;\n",
      "\tbackbone -- rhea;\n",
      "\tbackbone -- seasnake;\n",
      "\tbackbone -- skimmer;\n",
      "\tbackbone -- skua;\n",
      "\tbackbone -- slowworm;\n",
      "\tbackbone -- stingray;\n",
      "\tbackbone -- tuatara;\n",
      "\tbackbone -- tuna;\n",
      "\tbackbone -- vulture;\n",
      "\tbackbone -- carp;\n",
      "\tbackbone -- haddock;\n",
      "\tbackbone -- seahorse;\n",
      "\tbackbone -- sole;\n",
      "\tbackbone -- toad;\n",
      "\tbackbone -- chicken;\n",
      "\tbackbone -- dove;\n",
      "\tbackbone -- duck;\n",
      "\tbackbone -- flamingo;\n",
      "\tbackbone -- lark;\n",
      "\tbackbone -- ostrich;\n",
      "\tbackbone -- parakeet;\n",
      "\tbackbone -- pheasant;\n",
      "\tbackbone -- sparrow;\n",
      "\tbackbone -- swan;\n",
      "\tbackbone -- tortoise;\n",
      "\tbackbone -- wren;\n",
      "\tbreathes -- antelope;\n",
      "\tbreathes -- bear;\n",
      "\tbreathes -- boar;\n",
      "\tbreathes -- buffalo;\n",
      "\tbreathes -- calf;\n",
      "\tbreathes -- cavy;\n",
      "\tbreathes -- cheetah;\n",
      "\tbreathes -- deer;\n",
      "\tbreathes -- elephant;\n",
      "\tbreathes -- fruitbat;\n",
      "\tbreathes -- giraffe;\n",
      "\tbreathes -- girl;\n",
      "\tbreathes -- goat;\n",
      "\tbreathes -- gorilla;\n",
      "\tbreathes -- hamster;\n",
      "\tbreathes -- hare;\n",
      "\tbreathes -- honeybee;\n",
      "\tbreathes -- housefly;\n",
      "\tbreathes -- leopard;\n",
      "\tbreathes -- lion;\n",
      "\tbreathes -- lynx;\n",
      "\tbreathes -- mink;\n",
      "\tbreathes -- mole;\n",
      "\tbreathes -- mongoose;\n",
      "\tbreathes -- moth;\n",
      "\tbreathes -- opossum;\n",
      "\tbreathes -- oryx;\n",
      "\tbreathes -- platypus;\n",
      "\tbreathes -- polecat;\n",
      "\tbreathes -- pony;\n",
      "\tbreathes -- puma;\n",
      "\tbreathes -- pussycat;\n",
      "\tbreathes -- raccoon;\n",
      "\tbreathes -- reindeer;\n",
      "\tbreathes -- seal;\n",
      "\tbreathes -- sealion;\n",
      "\tbreathes -- squirrel;\n",
      "\tbreathes -- vampire;\n",
      "\tbreathes -- vole;\n",
      "\tbreathes -- wallaby;\n",
      "\tbreathes -- wasp;\n",
      "\tbreathes -- wolf;\n",
      "\tbreathes -- dolphin;\n",
      "\tbreathes -- porpoise;\n",
      "\tbreathes -- crow;\n",
      "\tbreathes -- frog;\n",
      "\tbreathes -- gull;\n",
      "\tbreathes -- hawk;\n",
      "\tbreathes -- kiwi;\n",
      "\tbreathes -- ladybird;\n",
      "\tbreathes -- newt;\n",
      "\tbreathes -- penguin;\n",
      "\tbreathes -- pitviper;\n",
      "\tbreathes -- rhea;\n",
      "\tbreathes -- scorpion;\n",
      "\tbreathes -- skimmer;\n",
      "\tbreathes -- skua;\n",
      "\tbreathes -- slowworm;\n",
      "\tbreathes -- tuatara;\n",
      "\tbreathes -- vulture;\n",
      "\tbreathes -- toad;\n",
      "\tbreathes -- chicken;\n",
      "\tbreathes -- dove;\n",
      "\tbreathes -- duck;\n",
      "\tbreathes -- flamingo;\n",
      "\tbreathes -- lark;\n",
      "\tbreathes -- ostrich;\n",
      "\tbreathes -- parakeet;\n",
      "\tbreathes -- pheasant;\n",
      "\tbreathes -- sparrow;\n",
      "\tbreathes -- swan;\n",
      "\tbreathes -- tortoise;\n",
      "\tbreathes -- wren;\n",
      "\tbreathes -- flea;\n",
      "\tbreathes -- gnat;\n",
      "\tbreathes -- slug;\n",
      "\tbreathes -- termite;\n",
      "\tbreathes -- worm;\n",
      "\t\"4_legs\" -- antelope;\n",
      "\t\"4_legs\" -- bear;\n",
      "\t\"4_legs\" -- boar;\n",
      "\t\"4_legs\" -- buffalo;\n",
      "\t\"4_legs\" -- calf;\n",
      "\t\"4_legs\" -- cavy;\n",
      "\t\"4_legs\" -- cheetah;\n",
      "\t\"4_legs\" -- deer;\n",
      "\t\"4_legs\" -- elephant;\n",
      "\t\"4_legs\" -- giraffe;\n",
      "\t\"4_legs\" -- goat;\n",
      "\t\"4_legs\" -- hamster;\n",
      "\t\"4_legs\" -- hare;\n",
      "\t\"4_legs\" -- leopard;\n",
      "\t\"4_legs\" -- lion;\n",
      "\t\"4_legs\" -- lynx;\n",
      "\t\"4_legs\" -- mink;\n",
      "\t\"4_legs\" -- mole;\n",
      "\t\"4_legs\" -- mongoose;\n",
      "\t\"4_legs\" -- opossum;\n",
      "\t\"4_legs\" -- oryx;\n",
      "\t\"4_legs\" -- platypus;\n",
      "\t\"4_legs\" -- polecat;\n",
      "\t\"4_legs\" -- pony;\n",
      "\t\"4_legs\" -- puma;\n",
      "\t\"4_legs\" -- pussycat;\n",
      "\t\"4_legs\" -- raccoon;\n",
      "\t\"4_legs\" -- reindeer;\n",
      "\t\"4_legs\" -- vole;\n",
      "\t\"4_legs\" -- wolf;\n",
      "\t\"4_legs\" -- crab;\n",
      "\t\"4_legs\" -- frog;\n",
      "\t\"4_legs\" -- newt;\n",
      "\t\"4_legs\" -- tuatara;\n",
      "\t\"4_legs\" -- toad;\n",
      "\t\"4_legs\" -- tortoise;\n",
      "\tcatsize -- antelope;\n",
      "\tcatsize -- bear;\n",
      "\tcatsize -- boar;\n",
      "\tcatsize -- buffalo;\n",
      "\tcatsize -- calf;\n",
      "\tcatsize -- cheetah;\n",
      "\tcatsize -- deer;\n",
      "\tcatsize -- elephant;\n",
      "\tcatsize -- giraffe;\n",
      "\tcatsize -- girl;\n",
      "\tcatsize -- goat;\n",
      "\tcatsize -- gorilla;\n",
      "\tcatsize -- leopard;\n",
      "\tcatsize -- lion;\n",
      "\tcatsize -- lynx;\n",
      "\tcatsize -- mink;\n",
      "\tcatsize -- mongoose;\n",
      "\tcatsize -- oryx;\n",
      "\tcatsize -- platypus;\n",
      "\tcatsize -- polecat;\n",
      "\tcatsize -- pony;\n",
      "\tcatsize -- puma;\n",
      "\tcatsize -- pussycat;\n",
      "\tcatsize -- raccoon;\n",
      "\tcatsize -- reindeer;\n",
      "\tcatsize -- seal;\n",
      "\tcatsize -- sealion;\n",
      "\tcatsize -- wallaby;\n",
      "\tcatsize -- wolf;\n",
      "\tcatsize -- dolphin;\n",
      "\tcatsize -- porpoise;\n",
      "\tcatsize -- dogfish;\n",
      "\tcatsize -- octopus;\n",
      "\tcatsize -- penguin;\n",
      "\tcatsize -- pike;\n",
      "\tcatsize -- rhea;\n",
      "\tcatsize -- stingray;\n",
      "\tcatsize -- tuna;\n",
      "\tcatsize -- vulture;\n",
      "\tcatsize -- flamingo;\n",
      "\tcatsize -- ostrich;\n",
      "\tcatsize -- swan;\n",
      "\tcatsize -- tortoise;\n",
      "\tMammal -- antelope;\n",
      "\tMammal -- bear;\n",
      "\tMammal -- boar;\n",
      "\tMammal -- buffalo;\n",
      "\tMammal -- calf;\n",
      "\tMammal -- cavy;\n",
      "\tMammal -- cheetah;\n",
      "\tMammal -- deer;\n",
      "\tMammal -- elephant;\n",
      "\tMammal -- fruitbat;\n",
      "\tMammal -- giraffe;\n",
      "\tMammal -- girl;\n",
      "\tMammal -- goat;\n",
      "\tMammal -- gorilla;\n",
      "\tMammal -- hamster;\n",
      "\tMammal -- hare;\n",
      "\tMammal -- leopard;\n",
      "\tMammal -- lion;\n",
      "\tMammal -- lynx;\n",
      "\tMammal -- mink;\n",
      "\tMammal -- mole;\n",
      "\tMammal -- mongoose;\n",
      "\tMammal -- opossum;\n",
      "\tMammal -- oryx;\n",
      "\tMammal -- platypus;\n",
      "\tMammal -- polecat;\n",
      "\tMammal -- pony;\n",
      "\tMammal -- puma;\n",
      "\tMammal -- pussycat;\n",
      "\tMammal -- raccoon;\n",
      "\tMammal -- reindeer;\n",
      "\tMammal -- seal;\n",
      "\tMammal -- sealion;\n",
      "\tMammal -- dolphin;\n",
      "\tMammal -- porpoise;\n",
      "\tantelope -- tail;\n",
      "\tcalf -- domestic;\n",
      "\tbass -- eggs;\n",
      "\tbass -- aquatic;\n",
      "\tbass -- fins;\n",
      "\tbass -- \"0_legs\";\n",
      "\tbass -- Fish;\n",
      "\tclam -- Invertebrate;\n",
      "\tcrayfish -- \"6_legs\";\n",
      "\tfrog -- Amphibian;\n",
      "\tfrog -- venomous;\n",
      "\toctopus -- \"8_legs\";\n",
      "\tpitviper -- Reptile;\n",
      "\tstarfish -- \"5_legs\";\n",
      "\tchicken -- feathers;\n",
      "\tchicken -- airborne;\n",
      "\tchicken -- \"2_legs\";\n",
      "\tchicken -- Bird;\n",
      "\tflea -- Bug;\n",
      "\ttail -- boar;\n",
      "\ttail -- buffalo;\n",
      "\ttail -- calf;\n",
      "\ttail -- cheetah;\n",
      "\ttail -- deer;\n",
      "\ttail -- elephant;\n",
      "\ttail -- fruitbat;\n",
      "\ttail -- giraffe;\n",
      "\ttail -- goat;\n",
      "\ttail -- hamster;\n",
      "\ttail -- hare;\n",
      "\ttail -- leopard;\n",
      "\ttail -- lion;\n",
      "\ttail -- lynx;\n",
      "\ttail -- mink;\n",
      "\ttail -- mole;\n",
      "\ttail -- mongoose;\n",
      "\ttail -- opossum;\n",
      "\ttail -- oryx;\n",
      "\ttail -- platypus;\n",
      "\ttail -- polecat;\n",
      "\ttail -- pony;\n",
      "\ttail -- puma;\n",
      "\ttail -- pussycat;\n",
      "\ttail -- raccoon;\n",
      "\ttail -- reindeer;\n",
      "\ttail -- sealion;\n",
      "\ttail -- squirrel;\n",
      "\ttail -- vampire;\n",
      "\ttail -- vole;\n",
      "\ttail -- wallaby;\n",
      "\ttail -- wolf;\n",
      "\ttail -- dolphin;\n",
      "\ttail -- porpoise;\n",
      "\ttail -- bass;\n",
      "\ttail -- catfish;\n",
      "\ttail -- chub;\n",
      "\ttail -- crow;\n",
      "\ttail -- dogfish;\n",
      "\ttail -- gull;\n",
      "\ttail -- hawk;\n",
      "\ttail -- herring;\n",
      "\ttail -- kiwi;\n",
      "\ttail -- newt;\n",
      "\ttail -- penguin;\n",
      "\ttail -- pike;\n",
      "\ttail -- piranha;\n",
      "\ttail -- pitviper;\n",
      "\ttail -- rhea;\n",
      "\ttail -- scorpion;\n",
      "\ttail -- seasnake;\n",
      "\ttail -- skimmer;\n",
      "\ttail -- skua;\n",
      "\ttail -- slowworm;\n",
      "\ttail -- stingray;\n",
      "\ttail -- tuatara;\n",
      "\ttail -- tuna;\n",
      "\ttail -- vulture;\n",
      "\ttail -- carp;\n",
      "\ttail -- haddock;\n",
      "\ttail -- seahorse;\n",
      "\ttail -- sole;\n",
      "\ttail -- chicken;\n",
      "\ttail -- dove;\n",
      "\ttail -- duck;\n",
      "\ttail -- flamingo;\n",
      "\ttail -- lark;\n",
      "\ttail -- ostrich;\n",
      "\ttail -- parakeet;\n",
      "\ttail -- pheasant;\n",
      "\ttail -- sparrow;\n",
      "\ttail -- swan;\n",
      "\ttail -- tortoise;\n",
      "\ttail -- wren;\n",
      "\teggs -- honeybee;\n",
      "\teggs -- housefly;\n",
      "\teggs -- moth;\n",
      "\teggs -- platypus;\n",
      "\teggs -- wasp;\n",
      "\teggs -- catfish;\n",
      "\teggs -- chub;\n",
      "\teggs -- clam;\n",
      "\teggs -- crab;\n",
      "\teggs -- crayfish;\n",
      "\teggs -- crow;\n",
      "\teggs -- dogfish;\n",
      "\teggs -- frog;\n",
      "\teggs -- gull;\n",
      "\teggs -- hawk;\n",
      "\teggs -- herring;\n",
      "\teggs -- kiwi;\n",
      "\teggs -- ladybird;\n",
      "\teggs -- lobster;\n",
      "\teggs -- newt;\n",
      "\teggs -- octopus;\n",
      "\teggs -- penguin;\n",
      "\teggs -- pike;\n",
      "\teggs -- piranha;\n",
      "\teggs -- pitviper;\n",
      "\teggs -- rhea;\n",
      "\teggs -- seawasp;\n",
      "\teggs -- skimmer;\n",
      "\teggs -- skua;\n",
      "\teggs -- slowworm;\n",
      "\teggs -- starfish;\n",
      "\teggs -- stingray;\n",
      "\teggs -- tuatara;\n",
      "\teggs -- tuna;\n",
      "\teggs -- vulture;\n",
      "\teggs -- carp;\n",
      "\teggs -- haddock;\n",
      "\teggs -- seahorse;\n",
      "\teggs -- sole;\n",
      "\teggs -- toad;\n",
      "\teggs -- chicken;\n",
      "\teggs -- dove;\n",
      "\teggs -- duck;\n",
      "\teggs -- flamingo;\n",
      "\teggs -- lark;\n",
      "\teggs -- ostrich;\n",
      "\teggs -- parakeet;\n",
      "\teggs -- pheasant;\n",
      "\teggs -- sparrow;\n",
      "\teggs -- swan;\n",
      "\teggs -- tortoise;\n",
      "\teggs -- wren;\n",
      "\teggs -- flea;\n",
      "\teggs -- gnat;\n",
      "\teggs -- slug;\n",
      "\teggs -- termite;\n",
      "\teggs -- worm;\n",
      "\taquatic -- mink;\n",
      "\taquatic -- platypus;\n",
      "\taquatic -- seal;\n",
      "\taquatic -- sealion;\n",
      "\taquatic -- dolphin;\n",
      "\taquatic -- porpoise;\n",
      "\taquatic -- catfish;\n",
      "\taquatic -- chub;\n",
      "\taquatic -- crab;\n",
      "\taquatic -- crayfish;\n",
      "\taquatic -- dogfish;\n",
      "\taquatic -- frog;\n",
      "\taquatic -- gull;\n",
      "\taquatic -- herring;\n",
      "\taquatic -- lobster;\n",
      "\taquatic -- newt;\n",
      "\taquatic -- octopus;\n",
      "\taquatic -- penguin;\n",
      "\taquatic -- pike;\n",
      "\taquatic -- piranha;\n",
      "\taquatic -- seasnake;\n",
      "\taquatic -- seawasp;\n",
      "\taquatic -- skimmer;\n",
      "\taquatic -- skua;\n",
      "\taquatic -- starfish;\n",
      "\taquatic -- stingray;\n",
      "\taquatic -- tuna;\n",
      "\taquatic -- carp;\n",
      "\taquatic -- haddock;\n",
      "\taquatic -- seahorse;\n",
      "\taquatic -- sole;\n",
      "\taquatic -- toad;\n",
      "\taquatic -- duck;\n",
      "\taquatic -- swan;\n",
      "\tfins -- seal;\n",
      "\tfins -- sealion;\n",
      "\tfins -- dolphin;\n",
      "\tfins -- porpoise;\n",
      "\tfins -- catfish;\n",
      "\tfins -- chub;\n",
      "\tfins -- dogfish;\n",
      "\tfins -- herring;\n",
      "\tfins -- pike;\n",
      "\tfins -- piranha;\n",
      "\tfins -- stingray;\n",
      "\tfins -- tuna;\n",
      "\tfins -- carp;\n",
      "\tfins -- haddock;\n",
      "\tfins -- seahorse;\n",
      "\tfins -- sole;\n",
      "\t\"0_legs\" -- seal;\n",
      "\t\"0_legs\" -- dolphin;\n",
      "\t\"0_legs\" -- porpoise;\n",
      "\t\"0_legs\" -- catfish;\n",
      "\t\"0_legs\" -- chub;\n",
      "\t\"0_legs\" -- clam;\n",
      "\t\"0_legs\" -- dogfish;\n",
      "\t\"0_legs\" -- herring;\n",
      "\t\"0_legs\" -- pike;\n",
      "\t\"0_legs\" -- piranha;\n",
      "\t\"0_legs\" -- pitviper;\n",
      "\t\"0_legs\" -- seasnake;\n",
      "\t\"0_legs\" -- seawasp;\n",
      "\t\"0_legs\" -- slowworm;\n",
      "\t\"0_legs\" -- stingray;\n",
      "\t\"0_legs\" -- tuna;\n",
      "\t\"0_legs\" -- carp;\n",
      "\t\"0_legs\" -- haddock;\n",
      "\t\"0_legs\" -- seahorse;\n",
      "\t\"0_legs\" -- sole;\n",
      "\t\"0_legs\" -- slug;\n",
      "\t\"0_legs\" -- worm;\n",
      "\tFish -- catfish;\n",
      "\tFish -- chub;\n",
      "\tFish -- dogfish;\n",
      "\tFish -- herring;\n",
      "\tFish -- pike;\n",
      "\tFish -- piranha;\n",
      "\tFish -- carp;\n",
      "\tFish -- haddock;\n",
      "\tFish -- seahorse;\n",
      "\tdomestic -- cavy;\n",
      "\tdomestic -- girl;\n",
      "\tdomestic -- goat;\n",
      "\tdomestic -- hamster;\n",
      "\tdomestic -- honeybee;\n",
      "\tdomestic -- pony;\n",
      "\tdomestic -- pussycat;\n",
      "\tdomestic -- reindeer;\n",
      "\tdomestic -- carp;\n",
      "\tdomestic -- chicken;\n",
      "\tdomestic -- dove;\n",
      "\tdomestic -- parakeet;\n",
      "\tfeathers -- crow;\n",
      "\tfeathers -- gull;\n",
      "\tfeathers -- hawk;\n",
      "\tfeathers -- kiwi;\n",
      "\tfeathers -- penguin;\n",
      "\tfeathers -- rhea;\n",
      "\tfeathers -- skimmer;\n",
      "\tfeathers -- skua;\n",
      "\tfeathers -- vulture;\n",
      "\tfeathers -- dove;\n",
      "\tfeathers -- duck;\n",
      "\tfeathers -- flamingo;\n",
      "\tfeathers -- lark;\n",
      "\tfeathers -- ostrich;\n",
      "\tfeathers -- parakeet;\n",
      "\tfeathers -- pheasant;\n",
      "\tfeathers -- sparrow;\n",
      "\tfeathers -- swan;\n",
      "\tfeathers -- wren;\n",
      "\tairborne -- fruitbat;\n",
      "\tairborne -- honeybee;\n",
      "\tairborne -- housefly;\n",
      "\tairborne -- moth;\n",
      "\tairborne -- vampire;\n",
      "\tairborne -- wasp;\n",
      "\tairborne -- crow;\n",
      "\tairborne -- gull;\n",
      "\tairborne -- hawk;\n",
      "\tairborne -- ladybird;\n",
      "\tairborne -- skimmer;\n",
      "\tairborne -- skua;\n",
      "\tairborne -- vulture;\n",
      "\tairborne -- dove;\n",
      "\tairborne -- duck;\n",
      "\tairborne -- flamingo;\n",
      "\tairborne -- lark;\n",
      "\tairborne -- parakeet;\n",
      "\tairborne -- pheasant;\n",
      "\tairborne -- sparrow;\n",
      "\tairborne -- swan;\n",
      "\tairborne -- wren;\n",
      "\tairborne -- gnat;\n",
      "\t\"2_legs\" -- fruitbat;\n",
      "\t\"2_legs\" -- girl;\n",
      "\t\"2_legs\" -- gorilla;\n",
      "\t\"2_legs\" -- sealion;\n",
      "\t\"2_legs\" -- squirrel;\n",
      "\t\"2_legs\" -- vampire;\n",
      "\t\"2_legs\" -- wallaby;\n",
      "\t\"2_legs\" -- crow;\n",
      "\t\"2_legs\" -- gull;\n",
      "\t\"2_legs\" -- hawk;\n",
      "\t\"2_legs\" -- kiwi;\n",
      "\t\"2_legs\" -- penguin;\n",
      "\t\"2_legs\" -- rhea;\n",
      "\t\"2_legs\" -- skimmer;\n",
      "\t\"2_legs\" -- skua;\n",
      "\t\"2_legs\" -- vulture;\n",
      "\t\"2_legs\" -- dove;\n",
      "\t\"2_legs\" -- duck;\n",
      "\t\"2_legs\" -- flamingo;\n",
      "\t\"2_legs\" -- lark;\n",
      "\t\"2_legs\" -- ostrich;\n",
      "\t\"2_legs\" -- parakeet;\n",
      "\t\"2_legs\" -- pheasant;\n",
      "\t\"2_legs\" -- sparrow;\n",
      "\t\"2_legs\" -- swan;\n",
      "\t\"2_legs\" -- wren;\n",
      "\tBird -- crow;\n",
      "\tBird -- gull;\n",
      "\tBird -- hawk;\n",
      "\tBird -- kiwi;\n",
      "\tBird -- penguin;\n",
      "\tBird -- rhea;\n",
      "\tBird -- skimmer;\n",
      "\tBird -- skua;\n",
      "\tBird -- dove;\n",
      "\tBird -- duck;\n",
      "\tBird -- flamingo;\n",
      "\tBird -- lark;\n",
      "\tBird -- ostrich;\n",
      "\tBird -- parakeet;\n",
      "\tBird -- pheasant;\n",
      "\tInvertebrate -- crab;\n",
      "\tInvertebrate -- crayfish;\n",
      "\tInvertebrate -- lobster;\n",
      "\tInvertebrate -- octopus;\n",
      "\tInvertebrate -- scorpion;\n",
      "\tInvertebrate -- seawasp;\n",
      "\t\"6_legs\" -- honeybee;\n",
      "\t\"6_legs\" -- housefly;\n",
      "\t\"6_legs\" -- moth;\n",
      "\t\"6_legs\" -- wasp;\n",
      "\t\"6_legs\" -- ladybird;\n",
      "\t\"6_legs\" -- lobster;\n",
      "\t\"6_legs\" -- flea;\n",
      "\t\"6_legs\" -- gnat;\n",
      "\t\"6_legs\" -- termite;\n",
      "\tBug -- honeybee;\n",
      "\tBug -- housefly;\n",
      "\tBug -- moth;\n",
      "\tBug -- ladybird;\n",
      "\tBug -- gnat;\n",
      "\tAmphibian -- newt;\n",
      "\tvenomous -- honeybee;\n",
      "\tvenomous -- wasp;\n",
      "\tvenomous -- pitviper;\n",
      "\tvenomous -- scorpion;\n",
      "\tvenomous -- seasnake;\n",
      "\tvenomous -- seawasp;\n",
      "\tvenomous -- stingray;\n",
      "\t\"8_legs\" -- scorpion;\n",
      "\tReptile -- seasnake;\n",
      "\tReptile -- slowworm;\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(A.string()) # print to screen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Vocabulary:\n",
    "    \"\"\"Vocabulary class. Wrapper for dictionaries\"\"\"\n",
    "    label2id = {}\n",
    "    id2label = {}\n",
    "    def lookup(self, word):\n",
    "        \"\"\"get word id; if not present, insert\"\"\"\n",
    "        if word in self.label2id:\n",
    "            return self.label2id[word]\n",
    "        ind = len(self.label2id)\n",
    "        self.label2id[word] = ind\n",
    "        return ind\n",
    "    \n",
    "    def inverse_lookup(self, index):\n",
    "        if len(self.id2label) == 0:\n",
    "            self.id2label = {\n",
    "                ind: label\n",
    "                for label, ind in self.label2id.items()\n",
    "            }\n",
    "        return self.id2label.get(index, None)\n",
    "        \n",
    "vocab = Vocabulary()\n",
    "nx_graph = nx.Graph()\n",
    "for (a, p, b) in triplets:\n",
    "    id1, id2 = vocab.lookup(a), vocab.lookup(b)\n",
    "    nx_graph.add_edge(id1, id2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'milk'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab.inverse_lookup(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx_graph.nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainamals = [\n",
    "    vocab.label2id[animal]\n",
    "    for animal in zoo.animal_name.values[:training_size]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "testimals = [\n",
    "    vocab.label2id[animal]\n",
    "    for animal in zoo.animal_name.values[training_size:]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "from karateclub.node_embedding.neighbourhood import Walklets\n",
    "\n",
    "model_w = Walklets(dimensions=5)\n",
    "model_w.fit(nx_graph)\n",
    "embedding = model_w.get_embedding()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine: Accuracy: 0.762\n",
      "[[5 0 0 0 0 0 0]\n",
      " [0 4 0 0 0 0 0]\n",
      " [2 0 0 1 0 0 0]\n",
      " [0 0 0 3 0 0 0]\n",
      " [1 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 2 0]\n",
      " [0 0 0 1 0 0 2]]\n"
     ]
    }
   ],
   "source": [
    "# Fit a support vector machine on train embeddings and evaluate on test\n",
    "clf = SVC(random_state=42)\n",
    "clf.fit(embedding[trainamals, :], zoo.class_type[:training_size])\n",
    "\n",
    "test_labels = zoo.class_type[training_size:]\n",
    "test_embeddings = embedding[testimals, :]\n",
    "print(end='Support Vector Machine: Accuracy: ')\n",
    "print('{:.3f}'.format(\n",
    "        accuracy_score(test_labels, clf.predict(test_embeddings))\n",
    "))\n",
    "print(confusion_matrix(test_labels, clf.predict(test_embeddings)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from karateclub import LabelPropagation\n",
    "\n",
    "model_lp = LabelPropagation()\n",
    "model_lp.fit(nx_graph)\n",
    "cluster_membership = model_lp.get_memberships()\n",
    "cluster_membership = np.array([cluster_membership[node] for node in range(len(cluster_membership))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 1)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_membership.reshape(-1, 1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine: Accuracy = 0.7619047619047619\n",
      "[[5 0 0 0 0 0 0]\n",
      " [0 4 0 0 0 0 0]\n",
      " [2 0 0 1 0 0 0]\n",
      " [0 0 0 3 0 0 0]\n",
      " [1 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 2 0]\n",
      " [0 0 0 1 0 0 2]]\n"
     ]
    }
   ],
   "source": [
    "clf = SVC(random_state=42)\n",
    "clf.fit(\n",
    "    np.concatenate(\n",
    "        [\n",
    "            #zoo.values[:training_size, 1:-1],\n",
    "            embedding[trainamals, :],\n",
    "            #cluster_membership[trainamals].reshape(-1, 1)\n",
    "        ],\n",
    "        axis=1\n",
    "    ),\n",
    "    zoo.class_type[:training_size]\n",
    ")\n",
    "\n",
    "test_labels = zoo.class_type[training_size:]\n",
    "test_embeddings = np.concatenate(\n",
    "    [\n",
    "        #zoo.values[training_size:, 1:-1],\n",
    "        embedding[testimals, :],\n",
    "        #cluster_membership[testimals].reshape(-1, 1)\n",
    "    ], axis=1\n",
    ")\n",
    "print(end='Support Vector Machine: Accuracy = ')\n",
    "print(accuracy_score(test_labels, clf.predict(test_embeddings)))\n",
    "print(confusion_matrix(test_labels, clf.predict(test_embeddings)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ff49fb705f8>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.manifold import TSNE\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "\n",
    "walk_tsne = TSNE(random_state=42)\n",
    "X_walk_tsne = walk_tsne.fit_transform(embedding[trainamals + testimals, :])\n",
    "\n",
    "# Define a color map\n",
    "\n",
    "palette = itertools.cycle(sns.color_palette())\n",
    "color_map = {}\n",
    "for i, label in enumerate(set(all_labels)):\n",
    "    color_map[label] = next(palette)\n",
    "\n",
    "# Plot the train embeddings\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.scatter(\n",
    "    X_walk_tsne[:training_size, 0],\n",
    "    X_walk_tsne[:training_size, 1],\n",
    "    edgecolors=[color_map[i] for i in all_labels],\n",
    "    facecolors='none',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Computing transition probabilities: 100%|██████████| 128/128 [00:00<00:00, 517.16it/s]\n"
     ]
    }
   ],
   "source": [
    "from node2vec import Node2Vec\n",
    "\n",
    "node2vec = Node2Vec(\n",
    "    nx_graph, \n",
    "    dimensions=9, \n",
    "    walk_length=30, \n",
    "    num_walks=200, \n",
    "    workers=4\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_n2v = node2vec.fit(window=10, min_count=1, batch_words=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.98137563, -1.5541772 ,  0.174435  ,  1.1330626 , -2.232591  ,\n",
       "       -0.28878462, -1.0754933 ,  0.90439093, -0.7833465 ], dtype=float32)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_n2v.wv.get_vector('0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_embeddings = np.concatenate(\n",
    "    [\n",
    "        model_n2v.wv.get_vector(\n",
    "            str(vocab.lookup(a))\n",
    "        ).reshape(1, -1)\n",
    "        for a in zoo.animal_name.values[:training_size]\n",
    "    ], axis=0\n",
    ")\n",
    "test_embeddings = np.concatenate(\n",
    "    [\n",
    "        model_n2v.wv.get_vector(\n",
    "            str(vocab.lookup(a))\n",
    "        ).reshape(1, -1)\n",
    "        for a in zoo.animal_name.values[training_size:]\n",
    "    ], axis=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 9)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_embeddings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.98137563, -1.5541772 ,  0.174435  ,  1.1330626 , -2.232591  ,\n",
       "       -0.28878462, -1.0754933 ,  0.90439093, -0.7833465 ], dtype=float32)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_embeddings[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine: Accuracy = 0.7619047619047619\n",
      "[[5 0 0 0 0 0 0]\n",
      " [0 4 0 0 0 0 0]\n",
      " [2 0 1 0 0 0 0]\n",
      " [0 0 0 3 0 0 0]\n",
      " [1 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 2 0]\n",
      " [0 0 0 2 0 0 1]]\n"
     ]
    }
   ],
   "source": [
    "clf = SVC(random_state=42)\n",
    "clf.fit(\n",
    "    np.concatenate(\n",
    "        [\n",
    "            #zoo.values[:training_size, 1:-1],\n",
    "            train_embeddings,\n",
    "            #cluster_membership[trainamals].reshape(-1, 1)\n",
    "        ],\n",
    "        axis=1\n",
    "    ),\n",
    "    zoo.class_type[:training_size]\n",
    ")\n",
    "\n",
    "test_labels = zoo.class_type[training_size:]\n",
    "test_embeddings = np.concatenate(\n",
    "    [\n",
    "        #zoo.values[training_size:, 1:-1],\n",
    "        test_embeddings,\n",
    "        #cluster_membership[testimals].reshape(-1, 1)\n",
    "    ], axis=1\n",
    ")\n",
    "print(end='Support Vector Machine: Accuracy = ')\n",
    "print(accuracy_score(test_labels, clf.predict(test_embeddings)))\n",
    "print(confusion_matrix(test_labels, clf.predict(test_embeddings)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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